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I'm a beginner in R, and trying to fit linear models with different intercepts per group, of the type y ~ A*x1 + B, where x1 is a numerical variable. I cannot understand whether I should use y1 ~ x1 +1 or y1 ~ order(x1) + 1 Although in the toy example included it makes a small difference, in models with many groups the models without order() converge slower if at all!

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

thunderbird just crashed, and I've never seen this before. Any idea what happened? this was in the dovecot log: 2010-12-02 13:06:40 imap-login: Info: Maximum number of connections from user+IP exceeded (mail_max_userip_connections): user=<pbc>, method=PLAIN, rip=127.0.0.1, lip=127.0.0.1, secured 2010-12-02 13:06:40 imap-login: Info: Maximum number of connections from user+IP exceeded

From: Ben Skeggs <bskeggs at redhat.com> We've previously had identical naming between vertex and texture formats, so it mostly made sense to define these together. However, upcoming patches are going to transition the driver over to using updated texture header definitions using NVIDIA's naming, and this will no longer be the case. Signed-off-by: Ben Skeggs <bskeggs at

I downloaded geepack from your site and installed it from the zip file. However, although it appears under my library directory, I could not load into R. Regards John Sendak platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 6.0 year 2002 month 10

Dear R-users: I can not reproduce figure 4 of *Statistical Tables and Plots using S and LATEX* by Prof. Frank Harrell with the following code: rm(list=ls()) library(Hmisc) getHdata(pbc) attach(pbc) age.groups <- cut2(age, c(45,60)) g <- function(y) apply(y, 2, quantile, c(.25,.5,.75)) y <- with(pbc, cbind(Chol=chol,Bili=bili)) # You can give new column names that are not legal S names

The data set in R is wrong. I've found mistakes on 2 lines in a quick look. I don't know if the data is incorrect in the Appendix of Fleming and Harrington as well (someone seems to have borrowed my copy), which is where the data set appears to have been taken from, given all the "-9" codes in it. (Note, Tom Fleming originally got the data from me, so I'm fairly

DeaR experts, I was asked for a log-rank pairwise survival comparison. I've a straightforward way to do this using the SAS system: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#/documentation/cdl/en/statug/63033/HTML/default/statug_lifetest_sect019.htm What I've found in R is shown below, but it's not a logrank test, I suppose. (The documentation

Hello, I'm attempting to use the ratetable argument to survexp in the survival package. I use the example from the ?survexp help page below, and then slightly modify it to produce an error. library(survival) data(pbc) #fit a model without any factors pfit1 <- coxph(Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + platelet, data=pbc) #this works as expected

I would like to determine the probability of an event at a specific timepoint given the linear predictor of a given Cox model. For instance, assume that I fit the following model: data(pbc) fit <- coxph(Surv(time, status)~ age, data=pbc) To extract the value of the linear predictor for each patient in the dataset: prd <- predict(fit, newdata=pbc, type="lp")

Hello, I have a question about the function lifetab in package KMsurv. The description of the output value surv says "the estimated survival function at the start of the intervals". Are these estimates the ones calculated via Kaplan-Meier probability of survival ? Thanks in advance! -- View this message in context:

Hi, When I run the following Cox proportional hazards model on the Mayo clinic's PBC data set (given in the "survival" package), the regression coefficients do not agree with the results presented in Table 4.6.3 (p. 195) of Fleming & Harrington's book. library(survival) data(pbc) ans.cox <- coxph(Surv(time, status) ~ log(bili) + log(alb) + age + log(protime) +

Hi - I am fitting various Cox PH models with spline predictors. After fitting the model, I would like to use termplot() to examine the functional form of the fitted model (e.g., to obtain a plot of the relative risk (or log r.r.) versus the predictors). When there is only 1 predictor in the model, termplot returns a "?". In this case, I have not been able to figure out how to create

Why not fix the new names instead to be like the old names? Seems like that would be way simpler... On Feb 15, 2016 12:38 AM, "Ben Skeggs" <skeggsb at gmail.com> wrote: > From: Ben Skeggs <bskeggs at redhat.com> > > We've previously had identical naming between vertex and texture > formats, so it mostly made sense to define these together. > > However,

Hi everyone, I am not new to R, but new to running survival models in R. I am trying to create some basic KM curves, using the following code: library(survival) library(KMsurv) (import data etc - basic right censored, with continuously observed time of death) sleepfit <- survfit(Surv(timeb, death), data = sleep) Here timeb is measured is survival in years, death is a 1/0 indicator (1 =

Well, I agree that plots with multiple y-axes can be very confusing but .... i am not sure that always they are also misleading .... One time i was asked to make a 2 y-axes plot, one y axes was for elevation (heights in meters) and one for rugosity (values between 1 and 2 - unitless). The idea was to visualize that for a more complex profile the rugosity is higher than for a flatter terrain. A

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

Hi there, Can anyone please help me because I am going to get crazy with the pbc data set. I just want to apply simple cox regression in the data set. I am a beginner in R but I don't think I am doing anything wrong. I have the book of Fleming and Harrington 1990. I perform cox regression by typing: out<- coxph(Surv(times/365,status)~log(bili)+log(proth)+edema+log(albumin)+age) out

On Sat, Dec 19, 2015 at 1:53 PM, Ilia Mirkin <imirkin at alum.mit.edu> wrote: > These are supported on GK20A and GM107. > > Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> > --- > > Was a bit torn on where to place the enums... we're about to gut all > the xml definitions so this seemed appropriate for now. > > Tested on GK20A only. > >

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous